从文本中提取分组的语义特征关系,以在社交媒体上表示药用植物属性知识
Chaveevan Pechsiri1, Intaka Piriyakul2, Joseph Santhi Pechsiri3
1College of Innovative Technology and Engineering, Dhurakij Pundit University, Bangkok, Thailand.
Frontiers in artificial intelligence
|August 27, 2025
概括
这项研究从泰国医学文本中提取了PlantPart-MedicinalPropertyGroup关系,创建了替代疗法的知识图. 这种方法有助于用户了解草药的特性,特别是在COVID-19等健康危机期间.
科学领域:
- 自然语言处理
- 生物信息学
- 植物学
背景情况:
- 药用植物知识对于替代疗法至关重要,尤其是在流行病期间.
- 泰国医学文本经常通过动词表达特征.
- 从这些文本中提取PlantPart-MedicinalPropertyGroup关系,由于语言复杂且缺乏注释,因此存在挑战.
研究的目的:
- 开发一种从药房学术网站中提取PlantPart-MedicinalPropertyGroup关系的方法.
- 创建药用植物属性知识的图形表示.
- 帮助本地用户和患者获得替代医疗信息.
主要方法:
- 使用来自HerbMed的Solving-Verb概念集来识别药物特性概念EDU.
- 应用单词共发生 (word-co) 模式以创建药物特性概念 (MPC) 表.
- 使用字符串匹配和结构方程建模来提取关系.
主要成果:
- 我们成功地提取了PlantPart-MedicinalPropertyGroup的高质量关系.
- 开发了一种方法以图形表示药用植物属性知识.
- 在没有明确的关系类标签的情况下证明了自动关系提取的可行性.
结论:
- 提出的方法有效地提取了有价值的药用植物知识.
- 这种方法可以提高对替代疗法的信息的可访问性.
- 图形表示有助于理解复杂的药用植物特性.
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